An introduction to parallel algorithms
An introduction to parallel algorithms
Maintaining B-trees on an EREW PRAM
Journal of Parallel and Distributed Computing
ACM Computing Surveys (CSUR)
A design of a parallel dictionary using skip lists
Theoretical Computer Science
Maple V: learning guide
Parallel dictionaries using AVL trees
Journal of Parallel and Distributed Computing - Parallel and distributed data structures
Parallel dictionaries with local rules on AVL and brother trees
Information Processing Letters
Concrete Mathematics: A Foundation for Computer Science
Concrete Mathematics: A Foundation for Computer Science
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Parallel Dictionaries in 2-3 Trees
Proceedings of the 10th Colloquium on Automata, Languages and Programming
The Binomial Transform and its Application to the Analysis of Skip Lists
ESA '95 Proceedings of the Third Annual European Symposium on Algorithms
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Fringe analysis uses the distribution of bottom subtrees or fringe of search trees under the assumption of random insertion of keys, yielding an average case analysis of the fringe. The results in the fringe give upper and lower bounds for several measures for the whole tree.We are interested in the fringe analysis of the synchronized parallel insertion algorithms of Paul, Vishkin, and Wagener (PVW) on 2-3 trees. This algorithm inserts k keys with k processors into a tree of size n with time O(log n + log k). As the direct analysis of this algorithm is very difficult we tackle this problem by introducing a new family of algorithms, denoted by MacroSplit algorithms, and our main theorem proves that two algorithms of this family, denoted MaxMacroSplit and MinMacroSplit, bound the behavior of the fringe in the PVW algorithm.Previous work deals with the fringe analysis of sequential algorithms, but this type of analysis was still an open problem for parallel algorithms on search trees. We extend fringe analysis to parallel algorithms and we get a rich mathematical structure giving new interpretations even in the sequential case. We prove that random insertion of keys generates a binomial distribution, that the synchronized insertion of keys can be modeled by a Markov chain, and that the coefficients of the transition matrix of the Markov chain are related to the expected local behavior of our algorithm. Finally, we show that the coefficients of the power expansion of this matrix over (n + 1)-1 are the binomial transform of the expected local behavior of the algorithm. We finally show that the fringe of the PVW algorithm asymptotically converges to the sequential case.